High speed communication systems capable of higher throughput data rates are emerging. Gigabit Ethernet networks may communicate information at 1 gigabits-per-second (Gbps) or higher over high speed channels. These high speed channels, however, typically realize a corresponding increase in error rates. Techniques such as forward error correction may be used to decrease the error rates. Such techniques, however, may require a communication system to communicate additional overhead in the form of error correcting information. The additional overhead may decrease the effective throughput for a communication system.
A typical physical communication channel, such as an Ethernet cable, for example, introduces inter-symbol interference (ISI) in a received data signal. To minimize the adverse effects of ISI and to improve signal-to-noise ratio (SNR), it is customary to include a filter in the receiver known as an “equalizer.” In some receivers, the entire equalizer may be adaptive. In such cases, however, convergence of the equalizer may be rather slow. In other receivers a fixed equalizer may be used in combination with an adaptive equalizer to provide improved convergence. Even with use of a combination of fixed and adaptive equalizers, however, convergence of the adaptive equalizer may be slower than desirable.
A communications system that includes passing a signal through a channel which introduces ISI and at least one additional interference signal (e.g., an echo signal, transmitted by near-end transmitter device, which has passed through an echo channel) in addition to jitter of the sampling clock at the analog-to-digital (A/D) converter in the receiver, results in a time-variant interference channel. This time-variant interference channel requires the adaptation of an interference canceller. An adaptive interference canceller adaptively filters a noise reference input to maximally match and subtract out noise or interference from a primary input signal (e.g., desired signal plus noise). In order to meet communications system performance requirements, it may be necessary to perform equalization and adaptation in order to reduce the ISI in the system. In addition, it may be necessary to include an interference canceller (e.g., an echo canceller) to cancel the interference described above. The interference canceller may be adapted using one of multiple adaptation processes and/or algorithms. For example, adaptation may be implemented using any of the well-known methods (e.g., least-mean-squares or LMS, recursive least squares or RLS, or Fast RLS).
These methods are well known adaptation algorithm. Briefly, an LMS adaptation algorithm, for example, uses an instantaneous estimate of a gradient vector of a cost function to generate an approximation of the steepest descent algorithm. The instantaneous estimate of the gradient is based on sampled values of a tap-input vector and an error signal. The algorithm iterates over each coefficient in the filter and moves it in the direction of the approximated gradient. The LMS algorithm requires a reference signal that represents the desired filter output. The difference between the reference signal and the actual output of the transversal filter is known as the error signal. An adaptation process employing the LMS algorithm determines a set of filter coefficients that minimize the expected value of a quadratic error signal, i.e., to achieve the least mean squared error (thus the name). As the LMS, RLS, and fast RLS are well known, no further details of these adaptation techniques are necessary for an understanding of the embodiments and examples described and illustrated herein.